Systems and methods for multi-contrast multi-scale vision transformers

Abstract

Methods and systems are provided for synthesizing a contrast-weighted image in Magnetic resonance imaging (MRI). The method comprises: receiving a multi-contrast image of a subject, where the multi-contrast image comprises one or more images of one or more different contrasts; generating an input to a transformer model based at least in part on the multi-contrast image; and generating, by the transformer model, a synthesized image having a target contrast that is different from the one or more different contrasts of the one or more images, where the target contrast is specified in a query received by the transformer model.

What is claimed is: 
     
         1 . A computer-implemented method for synthesizing a contrast-weighted image comprising:
 (a) receiving a multi-contrast image of a subject, wherein the multi-contrast image comprises one or more images of one or more different contrasts;   (b) generating an input to a transformer model based at least in part on the multi-contrast image; and   (c) generating, by the transformer model, a synthesized image having a target contrast that is different from the one or more different contrasts of the one or more images, wherein the target contrast is specified in a query received by the transformer model.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein the multi-contrast image is acquired using a magnetic resonance (MR) device. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein the input to the transformer model comprises an image encoding generated by a convolutional neural network (CNN) model. 
     
     
         4 . The computer-implemented method of  claim 3 , wherein the image encoding is partitioned into image patches. 
     
     
         5 . The computer-implemented method of  claim 3 , wherein the input to the transformer model comprises a combination of the image encoding and a contrast encoding. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein the transformer model comprises: i) an encoder model receiving the input and outputting multiple representations of the input having multiple scales, ii) a decoder model receiving the query and the multiple representations of the input having the multiple scales and outputting the synthesized image. 
     
     
         7 . The computer-implemented method of  claim 6 , wherein the encoder model comprises a multi-contrast shifted window-based attention block. 
     
     
         8 . The computer-implemented method of  claim 6 , wherein the decoder model comprises a multi-contrast shifted window-based attention block. 
     
     
         9 . The computer-implemented method of  claim 1 , wherein the transformer model is trained utilizing a combination of synthesis loss, reconstruction loss and adversarial loss. 
     
     
         10 . The computer-implemented method of  claim 1 , wherein the transformer model is trained utilizing multi-scale discriminators. 
     
     
         11 . The computer-implemented method of  claim 1 , wherein the transformer model is capable of taking arbitrary number of contrasts as input. 
     
     
         12 . The computer-implemented method of  claim 1 , further comprising displaying interpretation of the transformer model generating the synthesized image. 
     
     
         13 . The computer-implemented method of  claim 12 , wherein the interpretation is generated based at least in part on attention scores outputted by a decoder of the transformer model. 
     
     
         14 . The computer-implemented method of  claim 12 , wherein the interpretation comprises quantitative analysis of a contribution or importance of each of the one or more different contrasts. 
     
     
         15 . The computer-implemented method of  claim 12 , wherein the interpretation comprises a visual representation of the attention scores indicative a relevance of a region in the one or more images or a contrast from the one or more different contrasts to the synthesized image. 
     
     
         16 . A non-transitory computer-readable storage medium including instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
 (a) receiving a multi-contrast image of a subject, wherein the multi-contrast image comprises one or more images of one or more different contrasts;   (b) generating an input to a transformer model based at least in part on the multi-contrast image; and   (c) generating, by the transformer model, a synthesized image having a target contrast that is different from the one or more different contrasts of the one or more images, wherein the target contrast is specified in a query received by the transformer model.   
     
     
         17 . The non-transitory computer-readable storage medium of  claim 16 , wherein the multi-contrast image is acquired using a magnetic resonance (MR) device. 
     
     
         18 . The non-transitory computer-readable storage medium of  claim 16 , wherein the input to the transformer model comprises an image encoding generated by a convolutional neural network (CNN) model. 
     
     
         19 . The non-transitory computer-readable storage medium of  claim 18 , wherein the image encoding is partitioned into image patches. 
     
     
         20 . The non-transitory computer-readable storage medium of  claim 18 , wherein the input to the transformer model comprises a combination of the image encoding and a contrast encoding.